Shielding-based safe RL approaches typically use a safety-critic to estimate the safety of an action at the current state of the RL agent [1]}, [2]}. If any action is predicted to be unsafe, the alternative safe action is executed by the agent. Typically, the Bellman equation is used to update the safety critic with sampled transitions from the current policy. However, while training the safety-critic, the agent may violate the safety constraints. Moreover, these approaches are not data-efficient enough to use on physical robots.
